Modern classifiers use techniques which are highly complex when high accuracy classification is needed. For example, a traditional neural network structure needing high accuracy also needs a complex structure to perform classification because of difficulty in grouping different classes within the neural network structure.
Additionally, in speech recognition systems, when a spoken command is identified, the spoken command is identified as one of a group of commands represented by a collection of command models. Existing speech recognition systems require large amounts of processing and storage resources to identify a spoken command from a collection of command models because the systems fail to use ordering information (e.g., time-ordering) for training command models and for identifying spoken commands.
A problem with existing systems is that polynomial classifiers fail to use ordering information (e.g., time-ordering, event-ordering, characteristic-ordering, etc.) when performing identification of classes (e.g., spoken commands, phoneme identification, radio signatures, communication channels, etc.). Additionally, another problem with training systems for polynomial classifiers is that systems fail to train models using a method which exploits ordering information within training data.
Another problem with speech systems is the complexity of determining boundaries between spoken commands (e.g., words). The boundaries between spoken commands are important in speech recognition because boundaries are used to segment between spoken commands.
Existing systems also have a problem in detecting an acoustic context. For example, systems needing to detect an acoustic context have difficulty doing so because acoustic models of such systems fail to include ordering information. Failing to determine an acoustic context is a problem when a system needs to detect the onset and steady state of acoustic phones. Word spotting is similarly difficult to perform because existing systems exclude ordering information when determining word models.
Thus, what is needed are a system and method which use ordering information when performing identification of classes. What is also needed are a system and method which use ordering information contained within training data to train models.